# ONION: A Multi-Layered Framework for Participatory ER Design

arXiv: https://arxiv.org/abs/2507.08702 · HTML: https://mlsanity.com/paper/2507.08702/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 3 of 3)

- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [Participation in the age of foundation models](https://mlsanity.com/paper/2405.19479/) (2024)

## Built on by (most cited first, 1 of 1)

- [Memory Undone: Between Knowing and Not Knowing in Data Systems](https://mlsanity.com/paper/2602.21180/) (2026)

## Abstract (arXiv)

We present ONION, a multi-layered framework for participatory Entity-Relationship (ER) modeling that integrates insights from design justice, participatory AI, and conceptual modeling. ONION introduces a five-stage methodology: Observe, Nurture, Integrate, Optimize, Normalize. It supports progressive abstraction from unstructured stakeholder input to structured ER diagrams. Our approach aims to reduce designer bias, promote inclusive participation, and increase transparency through the modeling process. We evaluate ONION through real-world workshops focused on sociotechnical systems in Ukraine, highlighting how diverse stakeholder engagement leads to richer data models and deeper mutual understanding. Early results demonstrate ONION's potential to host diversity in early-stage data modeling. We conclude with lessons learned, limitations and challenges involved in scaling and refining the framework for broader adoption.